Prior attempts resort to weak supervision based on 2D silhouette annotations of monocular images to solve this task. For instance, Kanazawa . [1]} propose the use of more readily available 2D supervisions including keypoints as the supervision.
To further relax the supervision constraint, several follow-up studies propose to learn the 3D manifold in a self-supervised manner, only requiring single-view images and their corresponding masks for training [2]}, [3]}, [4]}, [5]}.
Minimizing the reconstruction error in the 2D domain tends to ignore the overall 3D geometry and back-side appearance, leading to a shortcut solution that may look plausible only from the input viewpoint, , SMR [5]} in Fig. REF  (a)(c).
While these methods compensate the relaxed supervision by exploiting various forms of prior information, , categorical semantic invariance [2]} and interpolated consistency of the predicted 3D attributes [5]}, this task remains challenging.
